A Hierarchical Evolutionary Algorithm for Constructing and Training Wavelet Networks

The wavelet network has been introduced as a special feed-forward neural network supported by the wavelet theory, and has become a popular tool in the approximation and forecast fields. In this paper, an evolutionary algorithm is proposed for constructing and training the wavelet network for approximation and forecast. This evolutionary algorithm utilises the hierarchical chromosome to encode the structure and parameters of the wavelet network, and combines a genetic algorithm and evolutionary programming to construct and train the network simultaneously through evolution. The numerical examples are presented to show the efficiency and potential of the proposed algorithm with respect to function approximation, sunspot time series forecast and condition forecast for a hydroturbine machine, respectively. The study also indicates that the proposed method has the potential to solve a wide range of neural network construction and training problems in a systematic and robust way.